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df_test <-
structure(list(pc.col = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("B", "C"), class = "factor"), pc.filler = structure(c(1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("1", "2", "3"), class = "factor"),
pc.batch = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L
), .Label = c("1", "2", "3", "4", "5", "6"), class = "factor"),
pc.op = structure(c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L), .Label = c("A",
"B", "C", "D"), class = "factor"), pc.volume = c(16.7533110462178,
18.0143546656987, 15.6448655396281, 18.0281678426422, 13.7831255488576,
16.758396178001, 14.6938147409883, 15.1974804312962, 14.2077591655389,
15.9579675459773, 18.1500426178447, 14.2297691617968, 16.8646535945654,
14.2798441018389, 16.1341256681412, 15.9241734353476, 16.8583005443759,
16.3449003481023, 15.4175473098922, 16.7861703759254, 15.3079007135867,
14.8169564636873, 17.2688507060631, 15.6874484539888), pc.density
= c(1.25341925113923,
1.11243453179479, 1.14110454507519, 1.09177192905336, 1.1465474843639,
1.12333920013556, 1.34554594406146, 1.18442400447752, 1.45756680703941,
1.18602487934004, 1.27641549258776, 1.28052785172529, 1.30119623444795,
1.11228530554194, 1.27060268503477, 1.34131613229472, 1.19909272287678,
1.19954395487512, 1.54100836814473, 1.31241568978863, 1.14210653222791,
1.21694093094929, 1.2603211001675, 1.32986554107345)), row.names = c(NA,
-24L), class = "data.frame")
Your factor structure 2x3x4x6=144 has 144 cells, but there are only 24
data points.
I am plotting both response variables against one factor at a time.
library(lattice)
?xyplot
bwplot(pc.volume + pc.density ~ pc.col + pc.filler + pc.batch + pc.op,
data=df_test, outer=TRUE)
bwplot(pc.volume + pc.density ~ pc.col, data=df_test, outer=TRUE)
bwplot(pc.volume + pc.density ~ pc.filler, data=df_test, outer=TRUE)
bwplot(pc.volume + pc.density ~ pc.batch, data=df_test, outer=TRUE)
bwplot(pc.volume + pc.density ~ pc.op, data=df_test, outer=TRUE)
More information about the experiment is needed before anything else
can be attempted.
Rich
On Wed, Mar 18, 2020 at 10:11 AM lionel sicot via R-help
<r-help@r-project.org> wrote:
>
> Hello,
> One of my colleagues sent me a csv file with 12 columns and a lot of rows.
> Column1 to Column10 are factors with 2 to 6 levels. Column11 and Column12 are
> experimental results.I'm a bit lost with all these data.
> I would like- to determine which factors have the most impact, and in which
> way, on Column10 (which has to be as high as possible) while Column 11, at
> the same time, has to be as low as possible (I hope it is clear for at least
> one of you ...).- to find a nice way to plot trends as there are several
> factors.
>
> Below is a small data.frame from the SixSigma package (4 columns of factors
> and 2 columns of values). I don't know if it can help you to show me how to
> "play" with my data.If not, a package name or a tutorial can also be hepful.
> Thanks in advance,Ptit Bleu.
> df_test<-read.table(text="pc.col pc.filler pc.batch pc.op pc.volume
> pc.densityC 1 1 A 16.7533110462178 1.25341925113923C 2 1 B 18.0143546656987
> 1.11243453179479C 3 1 C 15.6448655396281 1.14110454507519C 1 1 D
> 18.0281678426422 1.09177192905336C 2 2 A 13.7831255488576 1.1465474843639C 3
> 2 B 16.758396178001 1.12333920013556C 1 2 C 14.6938147409883
> 1.34554594406146C 2 2 D 15.1974804312962 1.18442400447752C 3 3 A
> 14.2077591655389 1.45756680703941C 1 3 B 15.9579675459773 1.18602487934004C 2
> 3 C 18.1500426178447 1.27641549258776C 3 3 D 14.2297691617968
> 1.28052785172529B 1 4 A 16.8646535945654 1.30119623444795B 2 4 B
> 14.2798441018389 1.11228530554194B 3 4 C 16.1341256681412 1.27060268503477B 1
> 4 D 15.9241734353476 1.34131613229472B 2 5 A 16.8583005443759
> 1.19909272287678B 3 5 B 16.3449003481023 1.19954395487512B 1 5 C
> 15.4175473098922 1.54100836814473B 2 5 D 16.7861703759254 1.31241568978863B 3
> 6 A 15.3079007135867 1.14210653222791B 1 6 B 14.8169564636873
> 1.21694093094929B 2 6 C 17.2688507
06
> 0631 1.2603211001675B 3 6 D 15.6874484539888 1.32986554107345", header=T)
>
>
> [[alternative HTML version deleted]]
>
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